Many new software applications, including those developed for mobile device platforms, use 3D computer vision processing to enhance their features, functionality, and user experience. One example of such vision processing is the detection of objects in three-dimensional space. Compared to traditional color image-based methods, 3D object detection techniques are more robust and more accurate. Specifically, 3D object detection techniques depend on the object's color and lighting of the scene, attributes which can cause unexpected problems for color image-based methods. Most importantly, 3D object detection methods provide the advantage of obtaining object orientation.
Unfortunately, traditional 3D object technology is time consuming and difficult to implement in the real world. The problem is that once the scans are captured and registered via Simultaneous Localization and Mapping (SLAM) (or Fusion) to create a model, the resulting 3D model is usually not closed (e.g., has holes or gaps) or noisy (e.g., not smooth with irregular surfaces). Hence, these scans are manually further processed using a CAD tool to create a fully closed and smooth 3D model which can then be 3D printed or used in animation.